Deforestation and CO2 Emission

import plotly.io as pio

pio.renderers.default = "vscode+jupyterlab+notebook_connected"

Deforestation and CO2 Emission

Deforestation has been increasing around the world, driven largely by industrialization, and it is playing a significant role in contributing to climate change. This issue has caught my attention, and I am interested in studying how deforestation impacts CO2 emissions. By understanding this connection, we can better grasp how deforestation acts as a driver of climate change.

Research Focus

1. Research Question:
   How does deforestation correlate with CO2 emissions globally from 2001 to 2019?

2. Hypothesis:
   Higher rates of deforestation contribute to increased CO2 emissions, with significant regional differences.

Data Sources

1. Forest Dataset:
   Sourced from Global Forest Watch, an organization dedicated to providing real-time data and tools for monitoring forests worldwide.

2. CO2 Emission Dataset:
   Obtained from Kaggle, which offers a dataset on CO2 emissions, growth, and population by country.

Step 1 - Import Library and Data Management

import pandas as pd
import plotly.express as px

forest = pd.read_csv("treecover_loss__ha_1.csv")
forest

	iso	umd_tree_cover_loss__year	umd_tree_cover_loss__ha
0	AFG	2001	88.092712
1	AGO	2001	101220.621525
2	AIA	2001	3.878461
3	ALA	2001	396.934826
4	ALB	2001	3729.021031
...	...	...	...
4566	XKO	2023	1465.438575
4567	XNC	2023	41.029104
4568	ZAF	2023	29571.219239
4569	ZMB	2023	190416.586825
4570	ZWE	2023	5690.371581

4571 rows × 3 columns

forest.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4571 entries, 0 to 4570
Data columns (total 3 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   iso                        4571 non-null   object 
 1   umd_tree_cover_loss__year  4571 non-null   int64  
 2   umd_tree_cover_loss__ha    4571 non-null   float64
dtypes: float64(1), int64(1), object(1)
memory usage: 107.3+ KB

Step 2 - Data Preprocessing:

• The forest dataset from Global Forest Watch includes a column named iso, which contains country codes following the International Organization for Standardization (ISO) standard. However, it does not include country names or additional specifications.

• To address this, we need to identify and load an additional dataset containing ISO codes alongside country names and specifications. This will allow us to link the forest data to corresponding countries for further analysis.

• Based on the output of the info() function, we can confirm that the data types for each column are appropriately formatted for analysis.

• To improve readability, we need to rename the column umd_tree_cover_loss__year to year.

forest.rename(columns={
    'umd_tree_cover_loss__year': 'year',
}, inplace=True)
forest

	iso	year	umd_tree_cover_loss__ha
0	AFG	2001	88.092712
1	AGO	2001	101220.621525
2	AIA	2001	3.878461
3	ALA	2001	396.934826
4	ALB	2001	3729.021031
...	...	...	...
4566	XKO	2023	1465.438575
4567	XNC	2023	41.029104
4568	ZAF	2023	29571.219239
4569	ZMB	2023	190416.586825
4570	ZWE	2023	5690.371581

4571 rows × 3 columns

Step 3 - Load the Continents Data

continents = pd.read_csv('continents2.csv')
continents

	name	alpha-2	alpha-3	country-code	iso_3166-2	region	sub-region	intermediate-region	region-code	sub-region-code	intermediate-region-code
0	Afghanistan	AF	AFG	4	ISO 3166-2:AF	Asia	Southern Asia	NaN	142.0	34.0	NaN
1	Åland Islands	AX	ALA	248	ISO 3166-2:AX	Europe	Northern Europe	NaN	150.0	154.0	NaN
2	Albania	AL	ALB	8	ISO 3166-2:AL	Europe	Southern Europe	NaN	150.0	39.0	NaN
3	Algeria	DZ	DZA	12	ISO 3166-2:DZ	Africa	Northern Africa	NaN	2.0	15.0	NaN
4	American Samoa	AS	ASM	16	ISO 3166-2:AS	Oceania	Polynesia	NaN	9.0	61.0	NaN
...	...	...	...	...	...	...	...	...	...	...	...
244	Wallis and Futuna	WF	WLF	876	ISO 3166-2:WF	Oceania	Polynesia	NaN	9.0	61.0	NaN
245	Western Sahara	EH	ESH	732	ISO 3166-2:EH	Africa	Northern Africa	NaN	2.0	15.0	NaN
246	Yemen	YE	YEM	887	ISO 3166-2:YE	Asia	Western Asia	NaN	142.0	145.0	NaN
247	Zambia	ZM	ZMB	894	ISO 3166-2:ZM	Africa	Sub-Saharan Africa	Eastern Africa	2.0	202.0	14.0
248	Zimbabwe	ZW	ZWE	716	ISO 3166-2:ZW	Africa	Sub-Saharan Africa	Eastern Africa	2.0	202.0	14.0

249 rows × 11 columns

• After loading the continent data, we can drop unnecessary columns and retain only the country (stored as name), region, and sub-region information based on the iso codes. Additionally, we need to rename the name column to country to ensure readability.

continents_region = continents[['alpha-3','name','region','sub-region']]
continents_region = continents[['alpha-3', 'name', 'region', 'sub-region']].copy()
continents_region.rename(columns={'region': 'continent_region',
                                  'name': 'country',
                                  'sub-region': 'continent_sub_region'}, inplace=True)
continents_region

	alpha-3	country	continent_region	continent_sub_region
0	AFG	Afghanistan	Asia	Southern Asia
1	ALA	Åland Islands	Europe	Northern Europe
2	ALB	Albania	Europe	Southern Europe
3	DZA	Algeria	Africa	Northern Africa
4	ASM	American Samoa	Oceania	Polynesia
...	...	...	...	...
244	WLF	Wallis and Futuna	Oceania	Polynesia
245	ESH	Western Sahara	Africa	Northern Africa
246	YEM	Yemen	Asia	Western Asia
247	ZMB	Zambia	Africa	Sub-Saharan Africa
248	ZWE	Zimbabwe	Africa	Sub-Saharan Africa

249 rows × 4 columns

Step 4 - Data Merging and Further Cleaning

We are now ready to merge the deforestation dataset with the continent data to include detailed regional information.

forest_region = pd.merge(
    forest,
    continents_region,
    left_on='iso',
    right_on='alpha-3',
    how='left'
)

forest_region.drop(columns=['alpha-3'], inplace=True)
forest_region

	iso	year	umd_tree_cover_loss__ha	country	continent_region	continent_sub_region
0	AFG	2001	88.092712	Afghanistan	Asia	Southern Asia
1	AGO	2001	101220.621525	Angola	Africa	Sub-Saharan Africa
2	AIA	2001	3.878461	Anguilla	Americas	Latin America and the Caribbean
3	ALA	2001	396.934826	Åland Islands	Europe	Northern Europe
4	ALB	2001	3729.021031	Albania	Europe	Southern Europe
...	...	...	...	...	...	...
4566	XKO	2023	1465.438575	NaN	NaN	NaN
4567	XNC	2023	41.029104	NaN	NaN	NaN
4568	ZAF	2023	29571.219239	South Africa	Africa	Sub-Saharan Africa
4569	ZMB	2023	190416.586825	Zambia	Africa	Sub-Saharan Africa
4570	ZWE	2023	5690.371581	Zimbabwe	Africa	Sub-Saharan Africa

4571 rows × 6 columns

As observed, there are still several ISO codes that are not included in our region dataset. To address this, we need to identify these missing codes. If possible, we should supplement the dataset by filling in the appropriate country names and regional information from other reliable sources.

nan_data = forest_region[forest_region['continent_region'].isna()]
nan_data

	iso	year	umd_tree_cover_loss__ha	country	continent_region	continent_sub_region
200	XAD	2001	1.648989	NaN	NaN	NaN
201	XCA	2001	9.735251	NaN	NaN	NaN
202	XKO	2001	1122.205429	NaN	NaN	NaN
203	XNC	2001	17.661583	NaN	NaN	NaN
407	XAD	2002	0.507202	NaN	NaN	NaN
...	...	...	...	...	...	...
4372	XKO	2022	784.685260	NaN	NaN	NaN
4373	XNC	2022	527.159030	NaN	NaN	NaN
4565	XCA	2023	0.989440	NaN	NaN	NaN
4566	XKO	2023	1465.438575	NaN	NaN	NaN
4567	XNC	2023	41.029104	NaN	NaN	NaN

87 rows × 6 columns

nan_data['iso'].unique()

array(['XAD', 'XCA', 'XKO', 'XNC'], dtype=object)

According to ISO 3166-1, codes starting with ‘X’ are reserved for user-assigned purposes and do not officially represent recognized countries. However, these codes are often used informally in datasets and applications to denote specific regions or entities. Here’s how the following codes are interpreted:

• XAD: Commonly used to denote Andorra.

• XKO: Typically used to represent Kosovo.

• XNC: Frequently stands for New Caledonia.

The code XCA appears incomplete or undocumented. As a result, I have decided to drop it from the analysis.

update_values = {
    'XAD': {'country': 'Andorra', 'continent_region': 'Europe', 'continent_sub_region': 'Southern Europe'},
    'XKO': {'country': 'Kosovo', 'continent_region': 'Europe', 'continent_sub_region': 'Southern Europe'},
    'XNC': {'country': 'New Caledonia', 'continent_region': 'Oceania', 'continent_sub_region': 'Melanesia'}
}

for iso, values in update_values.items():
    forest_region.loc[forest_region['iso'] == iso,
                      ['country', 'continent_region', 'continent_sub_region']] = values.values()
forest_region

	iso	year	umd_tree_cover_loss__ha	country	continent_region	continent_sub_region
0	AFG	2001	88.092712	Afghanistan	Asia	Southern Asia
1	AGO	2001	101220.621525	Angola	Africa	Sub-Saharan Africa
2	AIA	2001	3.878461	Anguilla	Americas	Latin America and the Caribbean
3	ALA	2001	396.934826	Åland Islands	Europe	Northern Europe
4	ALB	2001	3729.021031	Albania	Europe	Southern Europe
...	...	...	...	...	...	...
4566	XKO	2023	1465.438575	Kosovo	Europe	Southern Europe
4567	XNC	2023	41.029104	New Caledonia	Oceania	Melanesia
4568	ZAF	2023	29571.219239	South Africa	Africa	Sub-Saharan Africa
4569	ZMB	2023	190416.586825	Zambia	Africa	Sub-Saharan Africa
4570	ZWE	2023	5690.371581	Zimbabwe	Africa	Sub-Saharan Africa

4571 rows × 6 columns

forest_region = forest_region[forest_region['iso'] != 'XCA']
forest_region

	iso	year	umd_tree_cover_loss__ha	country	continent_region	continent_sub_region
0	AFG	2001	88.092712	Afghanistan	Asia	Southern Asia
1	AGO	2001	101220.621525	Angola	Africa	Sub-Saharan Africa
2	AIA	2001	3.878461	Anguilla	Americas	Latin America and the Caribbean
3	ALA	2001	396.934826	Åland Islands	Europe	Northern Europe
4	ALB	2001	3729.021031	Albania	Europe	Southern Europe
...	...	...	...	...	...	...
4566	XKO	2023	1465.438575	Kosovo	Europe	Southern Europe
4567	XNC	2023	41.029104	New Caledonia	Oceania	Melanesia
4568	ZAF	2023	29571.219239	South Africa	Africa	Sub-Saharan Africa
4569	ZMB	2023	190416.586825	Zambia	Africa	Sub-Saharan Africa
4570	ZWE	2023	5690.371581	Zimbabwe	Africa	Sub-Saharan Africa

4551 rows × 6 columns

forest_region[forest_region['continent_region'].isna()]

	iso	year	umd_tree_cover_loss__ha	country	continent_region	continent_sub_region

After verifying the result, we can confirm that there are no null values in the country column anymore. This ensures that all entries now have valid and complete country information.

Step 5 - Emission Data

We are now ready to load the emission dataset for further analysis.

emission = pd.read_csv("energy.csv")
emission

	Unnamed: 0	Country	Energy_type	Year	Energy_consumption	Energy_production	GDP	Population	Energy_intensity_per_capita	Energy_intensity_by_GDP	CO2_emission
0	0	World	all_energy_types	1980	292.899790	296.337228	27770.910281	4.298127e+06	68.145921	10.547000	4946.627130
1	1	World	coal	1980	78.656134	80.114194	27770.910281	4.298127e+06	68.145921	10.547000	1409.790188
2	2	World	natural_gas	1980	53.865223	54.761046	27770.910281	4.298127e+06	68.145921	10.547000	1081.593377
3	3	World	petroleum_n_other_liquids	1980	132.064019	133.111109	27770.910281	4.298127e+06	68.145921	10.547000	2455.243565
4	4	World	nuclear	1980	7.575700	7.575700	27770.910281	4.298127e+06	68.145921	10.547000	0.000000
...	...	...	...	...	...	...	...	...	...	...	...
55435	55435	Zimbabwe	coal	2019	0.045064	0.075963	37.620400	1.465420e+04	11.508701	4.482962	4.586869
55436	55436	Zimbabwe	natural_gas	2019	0.000000	0.000000	37.620400	1.465420e+04	11.508701	4.482962	0.000000
55437	55437	Zimbabwe	petroleum_n_other_liquids	2019	0.055498	0.000000	37.620400	1.465420e+04	11.508701	4.482962	4.377890
55438	55438	Zimbabwe	nuclear	2019	NaN	NaN	37.620400	1.465420e+04	11.508701	4.482962	0.000000
55439	55439	Zimbabwe	renewables_n_other	2019	0.068089	0.067499	37.620400	1.465420e+04	11.508701	4.482962	0.000000

55440 rows × 11 columns

emission.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 55440 entries, 0 to 55439
Data columns (total 11 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   Unnamed: 0                   55440 non-null  int64  
 1   Country                      55440 non-null  object 
 2   Energy_type                  55440 non-null  object 
 3   Year                         55440 non-null  int64  
 4   Energy_consumption           44287 non-null  float64
 5   Energy_production            44289 non-null  float64
 6   GDP                          40026 non-null  float64
 7   Population                   46014 non-null  float64
 8   Energy_intensity_per_capita  50358 non-null  float64
 9   Energy_intensity_by_GDP      50358 non-null  float64
 10  CO2_emission                 51614 non-null  float64
dtypes: float64(7), int64(2), object(2)
memory usage: 4.7+ MB

From the output of the info() function, we can see that the columns we intend to use—Country, CO2_emission, and Year—are stored in the appropriate dataset. To ensure consistency and avoid issues when merging the data, we will apply some minor formatting by renaming:

• Year to year

• Country to country

This step ensures uniform column naming across datasets.

emission.rename(columns={
    'Year': 'year',
    'Country': 'country'
}, inplace=True)
emission

	Unnamed: 0	country	Energy_type	year	Energy_consumption	Energy_production	GDP	Population	Energy_intensity_per_capita	Energy_intensity_by_GDP	CO2_emission
0	0	World	all_energy_types	1980	292.899790	296.337228	27770.910281	4.298127e+06	68.145921	10.547000	4946.627130
1	1	World	coal	1980	78.656134	80.114194	27770.910281	4.298127e+06	68.145921	10.547000	1409.790188
2	2	World	natural_gas	1980	53.865223	54.761046	27770.910281	4.298127e+06	68.145921	10.547000	1081.593377
3	3	World	petroleum_n_other_liquids	1980	132.064019	133.111109	27770.910281	4.298127e+06	68.145921	10.547000	2455.243565
4	4	World	nuclear	1980	7.575700	7.575700	27770.910281	4.298127e+06	68.145921	10.547000	0.000000
...	...	...	...	...	...	...	...	...	...	...	...
55435	55435	Zimbabwe	coal	2019	0.045064	0.075963	37.620400	1.465420e+04	11.508701	4.482962	4.586869
55436	55436	Zimbabwe	natural_gas	2019	0.000000	0.000000	37.620400	1.465420e+04	11.508701	4.482962	0.000000
55437	55437	Zimbabwe	petroleum_n_other_liquids	2019	0.055498	0.000000	37.620400	1.465420e+04	11.508701	4.482962	4.377890
55438	55438	Zimbabwe	nuclear	2019	NaN	NaN	37.620400	1.465420e+04	11.508701	4.482962	0.000000
55439	55439	Zimbabwe	renewables_n_other	2019	0.068089	0.067499	37.620400	1.465420e+04	11.508701	4.482962	0.000000

55440 rows × 11 columns

Step 6 - Final Data

data = pd.merge(emission, forest_region, on =["country", "year"], how='inner')
data

	Unnamed: 0	country	Energy_type	year	Energy_consumption	Energy_production	GDP	Population	Energy_intensity_per_capita	Energy_intensity_by_GDP	CO2_emission	iso	umd_tree_cover_loss__ha	continent_region	continent_sub_region
0	29112	Afghanistan	all_energy_types	2001	0.015914	0.007509	19.4201	21607.0	0.736543	0.819486	1.153149	AFG	88.092712	Asia	Southern Asia
1	29113	Afghanistan	coal	2001	0.000542	0.000515	19.4201	21607.0	0.736543	0.819486	0.001944	AFG	88.092712	Asia	Southern Asia
2	29114	Afghanistan	natural_gas	2001	0.001849	0.001849	19.4201	21607.0	0.736543	0.819486	0.451205	AFG	88.092712	Asia	Southern Asia
3	29115	Afghanistan	petroleum_n_other_liquids	2001	0.008037	0.000000	19.4201	21607.0	0.736543	0.819486	0.700000	AFG	88.092712	Asia	Southern Asia
4	29116	Afghanistan	nuclear	2001	NaN	NaN	19.4201	21607.0	0.736543	0.819486	0.000000	AFG	88.092712	Asia	Southern Asia
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
19207	55435	Zimbabwe	coal	2019	0.045064	0.075963	37.6204	14654.2	11.508701	4.482962	4.586869	ZWE	11553.329511	Africa	Sub-Saharan Africa
19208	55436	Zimbabwe	natural_gas	2019	0.000000	0.000000	37.6204	14654.2	11.508701	4.482962	0.000000	ZWE	11553.329511	Africa	Sub-Saharan Africa
19209	55437	Zimbabwe	petroleum_n_other_liquids	2019	0.055498	0.000000	37.6204	14654.2	11.508701	4.482962	4.377890	ZWE	11553.329511	Africa	Sub-Saharan Africa
19210	55438	Zimbabwe	nuclear	2019	NaN	NaN	37.6204	14654.2	11.508701	4.482962	0.000000	ZWE	11553.329511	Africa	Sub-Saharan Africa
19211	55439	Zimbabwe	renewables_n_other	2019	0.068089	0.067499	37.6204	14654.2	11.508701	4.482962	0.000000	ZWE	11553.329511	Africa	Sub-Saharan Africa

19212 rows × 15 columns

We have successfully joined the deforestation and emission datasets using an inner join. This ensures that only the matching data between the two datasets is retained. The merged data is now ready for exploratory data analysis.

Step 7 - Exploratory Data Analysis

Let’s begin by analyzing the trends in both deforestation and Food Security Index across different sub regions to get some nuances and find specific patterns.

forest_aggregate = data.groupby(['year', 'continent_region']).agg(
    {'umd_tree_cover_loss__ha': 'sum', 'CO2_emission': 'sum'}
).reset_index()

fig = px.line(
    forest_aggregate,
    x="year", 
    y="umd_tree_cover_loss__ha", 
    color="continent_region",
    labels={"year": "Year",
        "umd_tree_cover_loss__ha": "Tree Cover Loss (ha)",
        "continent_region": "Continent",
    },
    title="Deforestation by Region Over Time"
)
fig.show()

The chart highlights how deforestation varies across continents. The Americas show the highest levels of tree cover loss, with noticeable peaks in certain years that could reflect major deforestation events. Europe and Asia have moderate levels of deforestation, with some fluctuations over time. Africa shows a gradual but steady increase in tree cover loss, while Oceania remains relatively low throughout the period. These differences likely point to varying regional challenges and drivers behind deforestation.

fig = px.line(
    forest_aggregate,
    x="year", 
    y="CO2_emission", 
    color="continent_region",
    labels={"year": "Year",
        "CO2_emission": "CO2 Emission",
        "continent_region": "Continent",
    },
    title="CO2 Emission by Region Over Time"
)
fig.show()

Insights from the CO2 Emission by Region Over Time Chart

1. Asia:
   Asia’s CO2 emissions have been on a steady rise, making it the biggest contributor among all regions. This isn’t surprising given the rapid industrialization and energy demands of many countries in the region. As economies grow, so does the reliance on energy-intensive industries, which is clearly reflected in the numbers.

2. Americas:
   In the Americas, CO2 emissions have stayed relatively stable, with only small fluctuations over time. This seems to show a balance—on one hand, industrial activities remain significant, but on the other, there’s been progress in adopting renewable energy and other mitigation measures to keep emissions in check.

3. Europe:
   Europe’s emissions have either held steady or started to decline slightly. This likely reflects the impact of strong environmental policies and the gradual shift toward cleaner energy sources like wind and solar. It’s an example of how focused efforts can make a difference over time.

4. Africa:
   Emissions in Africa are still low compared to other regions, but they are slowly creeping up. This aligns with the ongoing industrialization and urbanization across the continent. As economies grow, energy use increases, which contributes to this upward trend.

5. Oceania:
   Oceania continues to record the lowest emissions of all the regions, and the numbers haven’t changed much over time. This could be because the region has fewer energy-intensive industries and a smaller population compared to places like Asia or the Americas.

Looking at the charts, we can see some interesting patterns when comparing deforestation and CO2 emissions across regions. In the Americas, deforestation has stayed consistently high over the years, with noticeable peaks around 2010 and 2018. However, what stands out is that CO2 emissions in the Americas haven’t followed the same trend, they’ve remained fairly stable. This suggests that other factors, like industrial activities or transportation, might be playing a bigger role in driving emissions here compared to deforestation.

Asia tells a different story. CO2 emissions have been steadily climbing, and there’s a moderate but consistent trend of deforestation as well. This could mean that activities like industrial expansion or agriculture, which often lead to deforestation, are also directly contributing to the region’s rising emissions.

Europe shows yet another dynamic. There’s very little deforestation happening, but CO2 emissions remain high and steady. This suggests that land-use changes aren’t a major factor for emissions in Europe; instead, it’s likely industries and transportation that are driving their numbers.

In Africa, there’s a gradual increase in both deforestation and CO2 emissions. This makes sense given population growth and the expansion of agriculture, which often go hand in hand with deforestation. Lastly, Oceania stands out with minimal deforestation and a relatively small contribution to global CO2 emissions, reflecting its limited overall impact.

What these charts show is that the relationship between deforestation and CO2 emissions varies a lot by region. In places like Asia and Africa, there’s a stronger connection between the two, while in regions like Europe and the Americas, emissions seem to be driven by other factors. It’s a reminder of how different each region’s story is when it comes to environmental challenges.

fig = px.scatter(
    data,
    x="umd_tree_cover_loss__ha",
    y="CO2_emission",
    color="continent_region",
    trendline="ols",
    title="Correlation Between Deforestation and CO2 Emission",
    labels={
        "umd_tree_cover_loss__ha": "Tree Cover Loss (ha)",
        "CO2_emission": "CO2 Emission",
        "continent_region": "Continent"
    }
)

fig.show()

The scatterplot tells an interesting story about how deforestation and CO2 emissions relate to one another across different regions. In Asia, we see the largest spread of deforestation values, paired with the highest CO2 emissions. This makes sense given the region’s rapid industrial growth and large-scale land-use changes, which are major contributors to emissions.

The Americas also show a noticeable pattern. While deforestation values vary, CO2 emissions are clustered in the mid to high range. This suggests that deforestation, along with other industrial activities, plays a significant role in driving emissions in this region.

Europe, on the other hand, has a weaker connection between deforestation and emissions. Even as deforestation varies, CO2 emissions stay relatively steady. This might reflect Europe’s success in adopting cleaner energy and stronger environmental policies that reduce reliance on activities that drive emissions.

In Africa, both deforestation and CO2 emissions are relatively low. This lines up with the region’s slower pace of industrialization and smaller-scale land-use changes. Finally, Oceania has the lowest levels of both deforestation and CO2 emissions, emphasizing its smaller global footprint.

What stands out from the plot is how the relationship between deforestation and emissions changes depending on the region. In Asia and the Americas, the link is clear, while in Europe, Africa, and Oceania, other factors seem to have a stronger influence. This shows that tackling deforestation’s impact on emissions requires a tailored approach for each region.

Step 8 - Correlation between Variables

data['umd_tree_cover_loss__ha'].describe()

count    1.921200e+04
mean     1.133450e+05
std      4.514337e+05
min      0.000000e+00
25%      2.502050e+02
50%      5.715100e+03
75%      4.494546e+04
max      5.560431e+06
Name: umd_tree_cover_loss__ha, dtype: float64

data['CO2_emission'].describe()

count    19071.000000
mean        59.369662
std        414.280960
min         -0.000138
25%          0.000000
50%          0.190589
75%         10.969902
max      10732.002367
Name: CO2_emission, dtype: float64

Looking at the numbers, both CO2 emissions and tree cover loss have distributions that are heavily skewed, with a few countries having extremely high values compared to the rest. For example, the maximum CO2 emission is over 10,000 metric tons, while the mean is only 59. This tells me that the average (mean) is being pulled up by these outliers, which doesn’t really represent what’s happening in most countries. The median, on the other hand, isn’t affected by these extremes, making it a much better choice for understanding the typical country’s emissions and deforestation. So, if I want to show a clearer picture of what’s happening at the country level, I’d definitely go with the median.

country_median = data.groupby('country').agg({
    'umd_tree_cover_loss__ha':'median',
    'CO2_emission':'median'
}).reset_index()
country_median

	country	umd_tree_cover_loss__ha	CO2_emission
0	Afghanistan	97.749480	0.188230
1	Albania	1188.248196	0.085429
2	Algeria	5763.909736	1.657544
3	Angola	161718.969611	0.226583
4	Antigua and Barbuda	25.463260	0.000000
...	...	...	...
173	Vanuatu	377.560462	0.000000
174	Venezuela	101285.582103	21.393005
175	Vietnam	132423.224113	17.462579
176	Zambia	84831.582310	0.067808
177	Zimbabwe	9631.632143	0.950000

178 rows × 3 columns

fig = px.scatter(
country_median,
x= 'umd_tree_cover_loss__ha',
y='CO2_emission',
text='country',
labels={
    'umd_tree_cover_loss__ha': 'Median Tree Cover Loss (ha)',
    'CO2_emission': 'Median CO2 Emission'},
title='Median Tree Cover Loss vs Median CO2 Emission by Country 2001 - 2019')

fig.update_traces(marker=dict(size=10, opacity=0.7), textposition='top center')
fig.update_layout(
title_font_size=16,
xaxis_title='Median Tree Cover Loss (ha)',
yaxis_title='Median CO2 Emission',
template='plotly_white'
)
fig.show()

This chart gives a fascinating look at how tree cover loss and CO2 emissions compare across countries from 2001 to 2019. Starting with the United States, it’s clear that while the U.S. has high CO2 emissions, its tree cover loss is relatively low compared to countries like Russia or Brazil. This suggests that most of the U.S.’s emissions come from industries like energy and transportation rather than deforestation.

China tells a similar story. It has some of the highest CO2 emissions but very little tree cover loss. This reflects the country’s reliance on heavy industry and fossil fuels to drive its rapid economic growth. On the other hand, Russia stands out with significant tree cover loss and moderate emissions. This could be due to logging and land-use changes contributing to its numbers.

Brazil and Indonesia, however, are different. Both countries show high levels of tree cover loss, but their CO2 emissions are not as high as those of industrial giants like the U.S. or China. For Brazil, the Amazon’s deforestation is a major factor, while in Indonesia, activities like palm oil plantations play a big role.

Then there’s the cluster of countries near the lower end of the chart, where both tree cover loss and emissions are minimal. These are likely smaller or less industrialized nations with limited impact on global emissions.

What this chart really shows is how different each country’s story is. For some, emissions are driven by industrialization, while for others, it’s deforestation and land-use changes that make the difference. It’s a reminder that tackling emissions requires tailored solutions that consider each country’s unique challenges.

Step 9 - Correlation in Each Region

from scipy.stats import pearsonr

results = []
for region, group in forest_aggregate.groupby('continent_region'):
    corr, p_value = pearsonr(group['umd_tree_cover_loss__ha'], group['CO2_emission'])
    results.append({
        'continent_region': region,
        'correlation': corr,
        'p_value': p_value,
        'is_significant': 'Yes' if p_value < 0.05 else 'No'
    })


correlations = pd.DataFrame(results)
correlations

	continent_region	correlation	p_value	is_significant
0	Africa	0.905043	1.015996e-07	Yes
1	Americas	-0.121429	6.204574e-01	No
2	Asia	0.766577	1.290445e-04	Yes
3	Europe	-0.578872	9.407491e-03	Yes
4	Oceania	0.390274	9.854547e-02	No

correlations['significance_color'] = correlations['is_significant'].map({'Yes': 'green', 'No': 'red'})

fig = px.bar(
    correlations,
    x='continent_region',
    y='correlation',
    title="Correlation Between Tree Cover Loss and CO2 Emissions by Region (Significance Highlighted)",
    labels={
        "continent_region": "Region",
        "correlation": "Correlation Coefficient"
    },
    color='is_significant',
    text='correlation',
    color_discrete_map={'Yes': 'green', 'No': 'red'}
)

fig.update_traces(
    texttemplate='%{text:.2f} (%{customdata[1]})',
    textposition='outside',
    customdata=correlations[['continent_region', 'is_significant']]
)

fig.update_layout(
    showlegend=True, 
    yaxis_title="Correlation Coefficient",
    legend_title="Significance",
)

fig.show()

The numbers tell an interesting story about how deforestation and CO2 emissions are connected in different regions. In Africa, the correlation is strong at 0.91, and it’s statistically significant, showing that deforestation has a clear and direct impact on emissions. Similarly, Asia has a strong and significant correlation of 0.77, reflecting how activities like land-use changes and industrial expansion drive emissions. Europe is quite different, with a negative correlation of -0.58, which is also significant. This likely shows the impact of successful policies like reforestation and the shift to cleaner energy. In the Americas, the correlation is weak at -0.12 and not significant, suggesting that emissions here are influenced more by industry and transportation rather than deforestation. Oceania also has a weak correlation of 0.39, and it’s not significant either, highlighting the region’s smaller role in these dynamics. These numbers really show how each region has its own unique relationship between deforestation and emissions.

from scipy.stats import linregress

regions = forest_aggregate.groupby('continent_region')

simple_regression_results = []


for region, group in regions:
    slope, intercept, r_value, p_value, std_err = linregress(
        group['umd_tree_cover_loss__ha'], group['CO2_emission']
    )
    
    simple_regression_results.append({
        'region': region,
        'slope': slope,
        'intercept': intercept,
        'r_squared': r_value**2,
        'p_value': p_value,
        'significant': p_value < 0.05
    })

simple_regression_df = pd.DataFrame(simple_regression_results)
print(simple_regression_df)

     region     slope     intercept  r_squared       p_value  significant
0    Africa  0.000065   1540.419573   0.819104  1.015996e-07         True
1  Americas -0.000005  15763.538362   0.014745  6.204574e-01        False
2      Asia  0.001006   9540.867791   0.587640  1.290445e-04         True
3    Europe -0.000037  13189.972045   0.335093  9.407491e-03         True
4   Oceania  0.000009    854.346422   0.152314  9.854547e-02        False

fig = px.bar(
    simple_regression_df,
    x='region',
    y='r_squared',
    color='significant',  # Highlight statistically significant regions
    title="R-squared and Statistical Significance by Region",
    labels={'region': 'Region', 'r_squared': 'R-squared Value'},
    text='r_squared'
)
fig.update_traces(texttemplate='%{text:.2f}', textposition='outside')
fig.show()

This chart paints a clear picture of how deforestation and CO2 emissions are connected across regions.

• Africa stands out with the highest R-squared value of 0.82, meaning deforestation explains a lot of the variation in emissions, and this relationship is statistically significant.

• In Asia, the R-squared is 0.59, which is still significant and shows a strong link between deforestation and emissions, likely tied to land-use changes and industrial growth.

• Europe is interesting because its R-squared is lower, at 0.34, but the connection is still significant, reflecting efforts like reforestation and cleaner energy.

• For the Americas, the story is different, its R-squared is barely 0.01, and the relationship isn’t significant, showing that emissions here are driven by other sectors like transportation and industry.

• Oceania has an R-squared of 0.14, which is also not significant, pointing to a weaker connection.

This really shows how the role of deforestation in emissions varies widely depending on the region and its unique dynamics.

Final Summary

When I set out to explore how deforestation correlates with CO2 emissions globally, I wanted to understand whether cutting down forests, often for agriculture or industrial purposes, is truly contributing to climate change. Looking at the data, the connection is clear in some regions but less so in others, showing that the relationship between deforestation and emissions is more complex than I initially thought.

In regions like Africa and Asia, the story is straightforward. Africa has the strongest link, with an R-squared of 0.82, meaning deforestation directly explains most of the CO2 emissions in the region. This makes sense, as land-use changes like agriculture are major contributors to emissions. Asia follows closely with an R-squared of 0.59, reflecting how industrial expansion and deforestation go hand in hand, fueling rising emissions. Both of these regions show statistically significant relationships, reinforcing the idea that deforestation is a key driver of CO2 emissions here.

In Europe, the connection is weaker, with an R-squared of 0.34, though still statistically significant. This suggests that while deforestation plays a role, policies like reforestation and sustainable land management have helped offset its impact on emissions. In contrast, the Americas and Oceania tell a very different story. The correlation is minimal, with R-squared values of 0.01 and 0.14, respectively, and neither is statistically significant. This points to other factors, like industrial and transportation emissions, playing a bigger role in these regions.

Deforestation is undoubtedly a driver of climate change, but its impact varies depending on the region. In places like Africa and Asia, where deforestation is closely tied to emissions, reducing forest loss could make a big difference in combating climate change. On the other hand, regions like Europe show how reforestation and strong environmental policies can weaken the link between deforestation and emissions. This journey from my initial question has shown me that tackling deforestation’s role in climate change requires region-specific strategies, balancing global solutions with local realities.

Collaboration and Sources

• This project was completed independently.

• I used ChatGPT to review my code, verify the logic, enhance data visualizations, and ensure the insights were clear and concise.